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Learning-To-Rank

Learning to rank is the application of machine learning to build ranking models. Some common use cases for ranking models are information retrieval (e.g., web search) and news feeds application (think Twitter, Facebook, Instagram).

Papers

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Showing 51–100 of 753 papers

TitleStatusHype
Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract)—0
Generative Pre-trained Ranking Model with Over-parameterization at Web-Scale (Extended Abstract)—0
LINKAGE: Listwise Ranking among Varied-Quality References for Non-Factoid QA Evaluation via LLMs—0
Patch Ranking: Efficient CLIP by Learning to Rank Local PatchesCode0
Understanding the Effects of the Baidu-ULTR Logging Policy on Two-Tower Models—0
A Framework for Ranking Content Providers Using Prompt Engineering and Self-Attention Network—0
Proximal Ranking Policy Optimization for Practical Safety in Counterfactual Learning to Rank—0
Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severity—0
Zeroshot Listwise Learning to Rank Algorithm for Recommendation—0
Efficient LLM Scheduling by Learning to RankCode2
Semi-Supervised Variational Adversarial Active Learning via Learning to Rank and Agreement-Based Pseudo Labeling—0
Baby Bear: Seeking a Just Right Rating Scale for Scalar Annotations—0
Contextual Dual Learning Algorithm with Listwise Distillation for Unbiased Learning to Rank—0
Ranking Across Different Content Types: The Robust Beauty of Multinomial Blending—0
Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading BanditsCode0
Pairwise Judgment Formulation for Semantic Embedding Model in Web Search—0
Set2Seq Transformer: Learning Permutation Aware Set Representations of Artistic Sequences—0
Calibration-Disentangled Learning and Relevance-Prioritized Reranking for Calibrated Sequential RecommendationCode0
Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank—0
Hidden or Inferred: Fair Learning-To-Rank with Unknown DemographicsCode0
MODRL-TA:A Multi-Objective Deep Reinforcement Learning Framework for Traffic Allocation in E-Commerce Search—0
Multi-objective Learning to Rank by Model Distillation—0
Leveraging Topic Specificity and Social Relationships for Expert Finding in Community Question Answering PlatformsCode0
Deep Domain Specialisation for single-model multi-domain learning to rank—0
When Search Engine Services meet Large Language Models: Visions and Challenges—0
Learning to Rank for Maps at Airbnb—0
Pistis-RAG: Enhancing Retrieval-Augmented Generation with Human Feedback—0
FIRST: Faster Improved Listwise Reranking with Single Token DecodingCode2
Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language ModelsCode2
Step-level Value Preference Optimization for Mathematical ReasoningCode3
Modeling User Retention through Generative Flow NetworksCode1
MrRank: Improving Question Answering Retrieval System through Multi-Result Ranking Model—0
Towards Explainable Test Case Prioritisation with Learning-to-Rank Models—0
GotFunding: A grant recommendation system based on scientific articles—0
Dual-Branch Network for Portrait Image Quality AssessmentCode1
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?Code0
Full Stage Learning to Rank: A Unified Framework for Multi-Stage Systems—0
RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank—0
Metalearners for Ranking Treatment Effects—0
Efficient and Responsible Adaptation of Large Language Models for Robust Top-k Recommendations—0
Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted TreesCode0
A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor SearchCode0
Learning to rank quantum circuits for hardware-optimized performance enhancement—0
Chiplet Placement Order Exploration Based on Learning to Rank with Graph Representation—0
Investigating the Robustness of Counterfactual Learning to Rank Models: A Reproducibility StudyCode0
Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search DatasetCode0
Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval—0
Learning to Rank Patches for Unbiased Image Redundancy ReductionCode0
RankingSHAP -- Listwise Feature Attribution Explanations for Ranking ModelsCode0
Metasql: A Generate-then-Rank Framework for Natural Language to SQL TranslationCode1
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